Forecast Chargeback Volume
“How many fraud-related chargebacks will each merchant generate in the next 3 months?”
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A real-world example
How many fraud-related chargebacks will each merchant generate in the next 3 months?
Card network monitoring programs (VDMP, MERC) fine acquirers $25K–$100K/month for merchants exceeding fraud chargeback thresholds. By the time you see last quarter’s numbers, you’re already on the hook. Predicting which merchants will breach lets you require reserves, tighten monitoring, or terminate early.
How KumoRFM solves this
Graph-powered fraud intelligence
Kumo correlates merchant transaction patterns, cardholder behavior across merchants, seasonal trends, and dispute history to forecast chargeback counts. It sees that Merchant M003 shares cardholders with high-dispute merchants — a cross-merchant signal invisible to per-merchant rules.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
Merchants
| merchant_id | merchant_name | mcc_code | acquiring_bank |
|---|---|---|---|
| M001 | QuickShop Inc | 5411 | First National |
| M002 | TravelNow | 4722 | First National |
| M003 | SafePay Ltd | 6012 | Metro Bank |
Chargebacks
| chargeback_id | merchant_id | card_id | amount | reason_code | timestamp |
|---|---|---|---|---|---|
| CB01 | M001 | CC42 | 89.99 | 10.4 | 2025-01-05 |
| CB02 | M001 | CC18 | 249.00 | 13.1 | 2025-01-12 |
| CB03 | M002 | CC07 | 1,200 | 10.4 | 2025-01-10 |
Write your PQL query
Describe what to predict in 2-3 lines — Kumo handles the rest
PREDICT COUNT(CHARGEBACKS.* WHERE CHARGEBACKS.REASON_CODE IN ("10.4", "13.1"), 0, 3, months) FOR EACH MERCHANTS.MERCHANT_ID
Prediction output
Every entity gets a score, updated continuously
| MERCHANT_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| M001 | 2025-02-01 | 312 |
| M002 | 2025-02-01 | 47 |
| M003 | 2025-02-01 | 589 |
Understand why
Every prediction includes feature attributions — no black boxes
Merchant M003 (SafePay Ltd)
Predicted: 589 chargebacks in 3 months
Top contributing features
Chargebacks (90d count)
142 chargebacks
39% attribution
Reason code 10.4 ratio
68%
24% attribution
Shared cardholders with high-dispute merchants
87 cards
19% attribution
MCC code
6012 (Financial)
11% attribution
Acquiring bank
Metro Bank
7% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2-3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Avoid $25K–$100K/month card network fines. Intervene on high-risk merchants before threshold breach. Reduce acquirer fraud exposure 20–35%.
Related scenarios
Explore more fraud predictions
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
